The fast Fourier transform (FFT) is the standard method that estimates the frequency components at equispaced locations. For non-equispaced locations, FFT is not useful and the discrete Fourier transform (DFT) is required.

Alternatively, NUFFT is the fast algorithm for calculating the frequency components at non-equispaced locations.

A minimal "getting start" tutorial is available at http://jyhmiinlin.github.io/pynufft/ .

PyNUFFT implements the min-max NUFFT of Fessler and Sutton, with the following features:

• Written in pure Python.
• Based on Python numerical libraries, such as Numpy, Scipy (matplotlib for displaying examples).
• Provides the Python interface including forward transform, adjoint transform and other routines.
• Provides 1D/2D/3D examples for further developments.
• (Experimental) support of NVIDIA's graphic processing units (GPUs) and opencl devices (GPUs or a multi-core CPU)
• Using tensor product so the memory on GPU can be reduced (patent pending)

PyNUFFT was funded by the Cambridge Commonwealth, European and International Trust (Cambridge, UK) and Ministry of Education, Taiwan.

I acknowledge the NVIDIA Corporation with the donation of a Titan X Pascal and a Quadro P6000 GPU used for developing the GPU code. Thanks to the authors of Michigan Image Reconstruction Toolbox (MIRT) for releasing the original min-max interpolator code. However, errors in PyNUFFT are not related to MIRT and please contact PyNUFFT service at [email protected] or open an issue.

The interpolator is designed using the Fessler and Sutton's min-max NUFFT algorithm: Fessler JA, Sutton BP. Nonuniform fast Fourier transforms using min-max interpolation. IEEE Trans Signal Process 2003;51(2):560-574.

If you find PyNUFFT useful, please cite:

Lin, Jyh-Miin. "Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)." Journal of Imaging 4.3 (2018): 51.

@article{lin2018python, title={Python Non-Uniform Fast {F}ourier Transform ({PyNUFFT}): An Accelerated Non-{C}artesian {MRI} Package on a Heterogeneous Platform ({CPU/GPU})}, author={Lin, Jyh-Miin}, journal={Journal of Imaging}, volume={4}, number={3}, pages={51}, year={2018}, publisher={Multidisciplinary Digital Publishing Institute} }